User privacy risk analysis for the Internet of Things

The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem

Security Analysis of the Masimo MightySat: Data Leakage to a Nosy Neighbor

Embedded technology known as the Internet of Things (IoT) has been integrated into everyday life, from the home, to the farm, industry, enterprise, the battlefield, and even for medical devices. With the increased use of networked devices comes an increased attack surface for malicious actors to gather and inject data, putting the privacy of users at risk. This research considers the Masimo MightySat fingertip pulse oximeter and the companion Masimo Professional Health app from a security standpoint, analyzing the Bluetooth Low Energy (BLE) communication from the device to the application and the data leakage between the two. It is found that with some analysis of a personally owned Masimo MightySat Rx through the use of an Ubertooth BLE traffic sniffer, static analysis of the HCI\_snoop.log and application data, and dynamic analysis of the app, data could be reasonably captured for another MightySat and interpret it to learn user health data

The Forensic Swing of Things: The Current Legal and Technical Challenges of IoT Forensics

The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data is stored on the devices and them being always connected makes them extremely volatile. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality)

The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service

Department of Management EngineeringAs communication and information technologies advance, the Internet of Things (IoT) has changed the way people live. In particular, as smart home security services have been widely commercialized, it is necessary to examine consumer perception. However, there is little research that explains the general perception of IoT and smart home services. This article will utilize communication privacy management theory and privacy calculus theory to investigate how options to protect privacy affect how users perceive benefits and costs and how those perceptions affect individuals??? intentions to use of smart home service. Scenario-based experiments were conducted, and perceived benefits and costs were treated as formative second-order constructs. The results of PLS analysis in the study showed that smart home options to protect privacy decreased perceived benefits and increased perceived costs. In addition, the perceived benefits and perceived costs significantly affected the intention to use smart home security services. This research contributes to the field of IoT and smart home research and gives practitioners notable guidelines.ope

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.Comment: Tp appear in the CCNC 2019 Conferenc

End-to-End Privacy for Open Big Data Markets

The idea of an open data market envisions the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviours of data owners and to generate additional business value using different techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special Issue Cloud Computing and the La

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols